❌ INSUFFICIENT VRAM ON SINGLE NVIDIA RTX 5080

DeepSeek-R1 671B (MoE) on NVIDIA RTX 5080 (16GB VRAM)

Even in 4-bit Q4_K_M, DeepSeek-R1 671B (MoE) requires 420.2 GB VRAM (27x NVIDIA RTX 5080 GPUs needed for full VRAM execution, or heavy CPU RAM offloading at ~3.4 tok/s).

🎛️ Interactive VRAM & KV-Cache Tuner

📊 Real-Time Memory Footprint

420.2 GB / 16 GB
🟦 Weights (419 GB) 🟪 KV Cache (0.54 GB) 🟨 Activation Buffer (0.66 GB)
Total Required VRAM420.2 GB
Available Hardware VRAM16 GB (NVIDIA RTX 5080)
VRAM Headroom / Overflow-404.20 GB
Estimated Generation Speed~3.4 tokens/sec
Architecture & AttentionMLA-MoE (61L, 8 KV heads)

🚀 Ready-to-Run Launch Command

ollama run deepseek-r1:671b
# Or with llama.cpp server (8K context, Flash Attention):
llama-server -hf deepseek-ai/DeepSeek-R1-GGUF -c 8192 -ngl 99 -fa
CLOUD GPU ALTERNATIVE

Need 420.2 GB+ VRAM to run DeepSeek-R1 671B (MoE)?

Recommended cloud setup for 4-bit: Multi-GPU H100 / H200 Node (or 1383.46 GB for full FP16). Spin up a pre-configured vLLM / Ollama instance in 60 seconds:

Provider Best For RTX 4090 (24GB) A100 (80GB) H100 (80GB) Bonus / Trial Action
RunPod 1-Click vLLM / Ollama Pods $0.44 / hr $1.39 / hr $2.39 / hr Instant PyTorch / vLLM Pods + Serverless GPU Deploy on RunPod →
Vast.ai Cheapest Spot GPU Market $0.29 / hr $0.95 / hr $1.79 / hr Save 50–70% on RTX 4090 / 5090 / A100 rentals Check Spot GPUs →
Vultr Cloud GPU Enterprise SLA & Bare Metal — $1.79 / hr $2.69 / hr $300 Free Credit for New Users Claim $300 Credit →
DigitalOcean GPU Droplets Managed H100 Droplets — — $2.99 / hr $200 Free Trial (60 Days) Get $200 Free →

All Quantization Formats: DeepSeek-R1 671B (MoE) on NVIDIA RTX 5080

Quantization Format Bits/Weight Weights VRAM KV Cache (8K) Total VRAM Fits on 1x NVIDIA RTX 5080 (16GB)? Est. Speed GPUs Needed
FP16 / BF16 (Unquantized) 16 bpw 1382.26 GB 0.54 GB 1383.46 GB No (Over by 1367.5GB) ~1.2 tok/s 87x NVIDIA RTX 5080
FP8 (W8A8 / E4M3) 8.5 bpw 734.33 GB 0.54 GB 735.52 GB No (Over by 719.5GB) ~2 tok/s 46x NVIDIA RTX 5080
GGUF Q8_0 (8-bit) 8.5 bpw 734.33 GB 0.54 GB 735.52 GB No (Over by 719.5GB) ~2 tok/s 46x NVIDIA RTX 5080
GGUF Q6_K (6-bit) 6.56 bpw 566.73 GB 0.54 GB 567.93 GB No (Over by 551.9GB) ~2.5 tok/s 36x NVIDIA RTX 5080
GGUF Q5_K_M (5-bit) 5.69 bpw 491.57 GB 0.54 GB 492.77 GB No (Over by 476.8GB) ~2.9 tok/s 31x NVIDIA RTX 5080
GGUF Q4_K_M / AWQ (4-bit) 4.85 bpw 419 GB 0.54 GB 420.2 GB No (Over by 404.2GB) ~3.4 tok/s 27x NVIDIA RTX 5080
GGUF IQ4_XS (4.25 bpw) 4.25 bpw 367.16 GB 0.54 GB 368.36 GB No (Over by 352.4GB) ~3.9 tok/s 24x NVIDIA RTX 5080
GGUF Q3_K_M (3-bit) 3.91 bpw 337.79 GB 0.54 GB 338.99 GB No (Over by 323.0GB) ~4.2 tok/s 22x NVIDIA RTX 5080

Frequently Asked Questions (DeepSeek-R1 671B (MoE) + NVIDIA RTX 5080)

Can NVIDIA RTX 5080 (16GB) run DeepSeek-R1 671B (MoE) locally?

No — A single NVIDIA RTX 5080 (16GB) cannot fit DeepSeek-R1 671B (MoE) in VRAM. Even in 4-bit Q4_K_M, DeepSeek-R1 671B (MoE) requires 420.2 GB VRAM (27x NVIDIA RTX 5080 GPUs needed for full VRAM execution, or heavy CPU RAM offloading at ~3.4 tok/s).

How is KV Cache calculated for DeepSeek-R1 671B (MoE)?

DeepSeek-R1 671B (MoE) uses MLA-MoE with 61 transformer layers, 128 attention heads, and 8 KV heads (head dimension 128). At 8,192 tokens in FP16, KV cache consumes 0.54 GB. Enabling 8-bit KV cache (-ctk q8_0 -ctv q8_0 in llama.cpp or --kv-cache-dtype fp8 in vLLM) cuts this in half to 0.27 GB.

Compare DeepSeek-R1 671B (MoE) on Other GPUs

DeepSeek-R1 671B (MoE) on NVIDIA RTX 5090 (32GB)DeepSeek-R1 671B (MoE) on NVIDIA RTX 4090 (24GB)DeepSeek-R1 671B (MoE) on NVIDIA RTX 4080 Super (16GB)DeepSeek-R1 671B (MoE) on NVIDIA RTX 4070 Ti Super (16GB)DeepSeek-R1 671B (MoE) on NVIDIA RTX 3090 (24GB)DeepSeek-R1 671B (MoE) on NVIDIA RTX 4060 Ti 16GB (16GB)DeepSeek-R1 671B (MoE) on NVIDIA RTX 3060 12GB (12GB)DeepSeek-R1 671B (MoE) on NVIDIA RTX 6000 Ada (48GB)DeepSeek-R1 671B (MoE) on NVIDIA RTX A6000 (48GB)DeepSeek-R1 671B (MoE) on NVIDIA L40S 48GB (48GB)

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